Bibliographic record
Abstract
Tables 1.1 Total privatization receipts for the OECD countries in the 1990s 5 3.1 International privatization by gross value, 1990-2000 43 6.1 Privatization receipts in the EU countries, 1990-2000 112 7.1 Major Canadian federal privatizations 132 7.2 Major Canadian provincial privatizations 138 7.3 The pre-and post-privatization performance of Canadian privatizations and other privatizations 144 7.4 Cumulative unadjusted and market-adjusted stock price returns for a portfolio of privatized Canadian corporations 148 8.1 Summary of divestitures in Australia in the 1990s 165 10.1 Summary of studies of firm-level impact of privatization in developing countries, 1980s to early 1990s 216 11.1 Brazil: total privatizations, by sector, 1990-2000 226 11.2 Brazil: ownership distribution of 100 largest firms, 1990-98 228 12.1 Distribution of productive assets in the Chinese agricultural sector, 1978 and 1985 236 12.2 Share of non-state enterprises in China's non-agricultural sector, 1980 and 1998 239 12.3 Employment in China's non-agricultural enterprises, 1998 240 12.4 Development of TVEs, 1984-98 241 12.5 Percentage share of private TVEs in total TVEs in China, 1984-97 243 12.6 Development of IEs in China, 1981-97 244 12.7 Development of PEs in China, 1989-97 246 12.8 Percentage contribution to GDP growth, by sectors in China 251 12.9 Regression results on determinants of China's provincial growth, 1978-85 252 12.10 Regression results on growth determinants of China's provinces, 1992-98 254 13.1 Main privatizations in Mexico since 1991 270 13.2 Performance indicators for the Mexican banking system 273 13.3 Mexico: real interest rates and financial margins, 1987-96 274 13.4 Performance of Chile's long-distance operators in early 1995 285 15.1 Privatizations and other SOE divestitures in SSA, 1985-95 311 15.2 Value of privatization transactions, by sub-region, in SSA, 1996-99
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.858 | 0.718 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".